Atomistic simulation of batteries via machine learning force fields: from bulk to interface

Abstract

Batteries play a critical role in electric vehicles and distributed energy generation. With the growing demand for energy storage solutions, new battery materials and systems are continually being developed. In this process, molecular dynamics (MD) simulations can reveal the microscopic mechanisms of battery processes, thereby boosting the design of batteries. Compared to other MD simulation techniques, the machine learning force field (MLFF) holds the advantages of first-principles accuracy along with large spatial and temporal scale, offering opportunities to uncover new mechanisms in battery systems. This review presents a detailed overview of the fundamental principles and model types of MLFFs, as well as their applications in simulating the structure, transport properties, and chemical reaction properties of bulk battery materials and interfaces. Notably, we emphasize the long-range interaction corrections and constant-potential methods in the model design of MLFFs. Finally, we discuss the challenges and prospects of applying MLFF models in the research of batteries.